Comparisons with the existing GNS model. Notice that the filter \(\mathbf {F}_c\)  itself already contains the source node information \(\mathbf {h}_s^{(k)}\) , and may be directly aggregated, as done in the off-the-shelf GNS models [1]}, [2]}.
Nonetheless, we empirically find that explicitly applying \(\mathbf {F}_c\)  on \(\mathbf {F}_d\)  through the element-wise dot product in Equation (REF ) significantly improves the performance.
There are also other subtle but important differences between ForceNet and the exising GNS model, such as the use of basis functions (Section REF ) and the choice of non-linear activations (Section REF ).
We empirically show that these careful architecture design choices in ForceNet contribute to the significant performance improvement over the existing GNS model.
